[{"quality_controlled":"1","file_date_updated":"2020-07-14T12:45:12Z","publisher":"Public Library of Science","scopus_import":1,"_id":"1697","issue":"7","author":[{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"first_name":"Vicente","last_name":"Botella Soler","orcid":"0000-0002-8790-1914","full_name":"Botella Soler, Vicente","id":"421234E8-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Simmons","first_name":"Kristina","full_name":"Simmons, Kristina"},{"last_name":"Mora","first_name":"Thierry","full_name":"Mora, Thierry"},{"last_name":"Tkacik","first_name":"Gasper","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Berry, Michael","last_name":"Berry","first_name":"Michael"}],"department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:53:31Z","publication_status":"published","intvolume":"        11","pubrep_id":"455","title":"High accuracy decoding of dynamical motion from a large retinal population","volume":11,"acknowledgement":"This work was supported by grants EY 014196 and EY 017934 to MJB, ANR OPTIMA, the French State program Investissements d’Avenir managed by the Agence Nationale de la Recherche [LIFESENSES: ANR-10-LABX-65], and by a EC grant from the Human Brain Project (CLAP) to OM, the Austrian Research Foundation FWF P25651 to VBS and GT. VBS is partially supported by contracts MEC, Spain (Grant No. AYA2010- 22111-C03-02, Grant No. AYA2013-48623-C2-2 and FEDER Funds).","ddc":["570"],"citation":{"ama":"Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. High accuracy decoding of dynamical motion from a large retinal population. <i>PLoS Computational Biology</i>. 2015;11(7). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004304\">10.1371/journal.pcbi.1004304</a>","apa":"Marre, O., Botella Soler, V., Simmons, K., Mora, T., Tkačik, G., &#38; Berry, M. (2015). High accuracy decoding of dynamical motion from a large retinal population. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1004304\">https://doi.org/10.1371/journal.pcbi.1004304</a>","ieee":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, and M. Berry, “High accuracy decoding of dynamical motion from a large retinal population,” <i>PLoS Computational Biology</i>, vol. 11, no. 7. Public Library of Science, 2015.","chicago":"Marre, Olivier, Vicente Botella Soler, Kristina Simmons, Thierry Mora, Gašper Tkačik, and Michael Berry. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” <i>PLoS Computational Biology</i>. Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pcbi.1004304\">https://doi.org/10.1371/journal.pcbi.1004304</a>.","short":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, M. Berry, PLoS Computational Biology 11 (2015).","mla":"Marre, Olivier, et al. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” <i>PLoS Computational Biology</i>, vol. 11, no. 7, e1004304, Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004304\">10.1371/journal.pcbi.1004304</a>.","ista":"Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. 2015. High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. 11(7), e1004304."},"year":"2015","date_updated":"2021-01-12T06:52:35Z","day":"01","doi":"10.1371/journal.pcbi.1004304","abstract":[{"lang":"eng","text":"Motion tracking is a challenge the visual system has to solve by reading out the retinal population. It is still unclear how the information from different neurons can be combined together to estimate the position of an object. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar’s position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. We then took advantage of this unprecedented precision to explore the spatial structure of the retina’s population code. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar’s position. Instead, we found that most ganglion cells in the salamander fired sparsely and idiosyncratically, so that their neural image did not track the bar. Furthermore, ganglion cell activity spanned an area much larger than predicted by their receptive fields, with cells coding for motion far in their surround. As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision. This organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits."}],"language":[{"iso":"eng"}],"has_accepted_license":"1","publication":"PLoS Computational Biology","project":[{"_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes"}],"oa_version":"Published Version","article_number":"e1004304","month":"07","file":[{"file_id":"5212","creator":"system","relation":"main_file","access_level":"open_access","date_updated":"2020-07-14T12:45:12Z","file_name":"IST-2016-455-v1+1_journal.pcbi.1004304.pdf","content_type":"application/pdf","date_created":"2018-12-12T10:16:25Z","file_size":4673930,"checksum":"472b979f3f1cffb37b3e503f085115ca"}],"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"type":"journal_article","date_published":"2015-07-01T00:00:00Z","oa":1,"publist_id":"5447"},{"publication":"PNAS","oa_version":"Submitted Version","project":[{"grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes","_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"month":"09","language":[{"iso":"eng"}],"date_published":"2015-09-15T00:00:00Z","type":"journal_article","publist_id":"5440","oa":1,"main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4577210/"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","pmid":1,"_id":"1701","scopus_import":1,"author":[{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Mora","first_name":"Thierry","full_name":"Mora, Thierry"},{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"full_name":"Amodei, Dario","first_name":"Dario","last_name":"Amodei"},{"first_name":"Stephanie","last_name":"Palmer","full_name":"Palmer, Stephanie"},{"first_name":"Michael","last_name":"Berry Ii","full_name":"Berry Ii, Michael"},{"full_name":"Bialek, William","first_name":"William","last_name":"Bialek"}],"issue":"37","publication_status":"published","department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:53:33Z","title":"Thermodynamics and signatures of criticality in a network of neurons","intvolume":"       112","page":"11508 - 11513","quality_controlled":"1","publisher":"National Academy of Sciences","date_updated":"2021-01-12T06:52:37Z","citation":{"chicago":"Tkačik, Gašper, Thierry Mora, Olivier Marre, Dario Amodei, Stephanie Palmer, Michael Berry Ii, and William Bialek. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” <i>PNAS</i>. National Academy of Sciences, 2015. <a href=\"https://doi.org/10.1073/pnas.1514188112\">https://doi.org/10.1073/pnas.1514188112</a>.","ieee":"G. Tkačik <i>et al.</i>, “Thermodynamics and signatures of criticality in a network of neurons,” <i>PNAS</i>, vol. 112, no. 37. National Academy of Sciences, pp. 11508–11513, 2015.","ama":"Tkačik G, Mora T, Marre O, et al. Thermodynamics and signatures of criticality in a network of neurons. <i>PNAS</i>. 2015;112(37):11508-11513. doi:<a href=\"https://doi.org/10.1073/pnas.1514188112\">10.1073/pnas.1514188112</a>","apa":"Tkačik, G., Mora, T., Marre, O., Amodei, D., Palmer, S., Berry Ii, M., &#38; Bialek, W. (2015). Thermodynamics and signatures of criticality in a network of neurons. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1514188112\">https://doi.org/10.1073/pnas.1514188112</a>","ista":"Tkačik G, Mora T, Marre O, Amodei D, Palmer S, Berry Ii M, Bialek W. 2015. Thermodynamics and signatures of criticality in a network of neurons. PNAS. 112(37), 11508–11513.","mla":"Tkačik, Gašper, et al. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” <i>PNAS</i>, vol. 112, no. 37, National Academy of Sciences, 2015, pp. 11508–13, doi:<a href=\"https://doi.org/10.1073/pnas.1514188112\">10.1073/pnas.1514188112</a>.","short":"G. Tkačik, T. Mora, O. Marre, D. Amodei, S. Palmer, M. Berry Ii, W. Bialek, PNAS 112 (2015) 11508–11513."},"year":"2015","external_id":{"pmid":["26330611"]},"doi":"10.1073/pnas.1514188112","day":"15","abstract":[{"lang":"eng","text":"The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance. "}],"volume":112,"acknowledgement":"Research was supported in part by National Science Foundation Grants PHY-1305525, PHY-1451171, and CCF-0939370, by National Institutes of Health Grant R01 EY14196, and by Austrian Science Foundation Grant FWF P25651. Additional support was provided by the\r\nFannie and John Hertz Foundation, by the Swartz Foundation, by the W. M. Keck Foundation, and by the Simons Foundation."},{"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_published":"2015-03-23T00:00:00Z","type":"journal_article","publist_id":"5278","oa":1,"file":[{"relation":"main_file","access_level":"open_access","creator":"system","file_id":"5161","checksum":"b8aa66f450ff8de393014b87ec7d2efb","file_size":1811647,"date_created":"2018-12-12T10:15:39Z","content_type":"application/pdf","file_name":"IST-2016-452-v1+1_journal.pcbi.1004055.pdf","date_updated":"2020-07-14T12:45:17Z"}],"related_material":{"record":[{"status":"public","id":"9718","relation":"research_data"},{"id":"9773","relation":"research_data","status":"public"}]},"status":"public","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publication":"PLoS Computational Biology","has_accepted_license":"1","oa_version":"Published Version","project":[{"grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"month":"03","language":[{"iso":"eng"}],"date_updated":"2023-02-23T14:07:51Z","citation":{"ista":"Friedlander T, Mayo A, Tlusty T, Alon U. 2015. Evolution of bow-tie architectures in biology. PLoS Computational Biology. 11(3).","short":"T. Friedlander, A. Mayo, T. Tlusty, U. Alon, PLoS Computational Biology 11 (2015).","mla":"Friedlander, Tamar, et al. “Evolution of Bow-Tie Architectures in Biology.” <i>PLoS Computational Biology</i>, vol. 11, no. 3, Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055\">10.1371/journal.pcbi.1004055</a>.","chicago":"Friedlander, Tamar, Avraham Mayo, Tsvi Tlusty, and Uri Alon. “Evolution of Bow-Tie Architectures in Biology.” <i>PLoS Computational Biology</i>. Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055\">https://doi.org/10.1371/journal.pcbi.1004055</a>.","ieee":"T. Friedlander, A. Mayo, T. Tlusty, and U. Alon, “Evolution of bow-tie architectures in biology,” <i>PLoS Computational Biology</i>, vol. 11, no. 3. Public Library of Science, 2015.","ama":"Friedlander T, Mayo A, Tlusty T, Alon U. Evolution of bow-tie architectures in biology. <i>PLoS Computational Biology</i>. 2015;11(3). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055\">10.1371/journal.pcbi.1004055</a>","apa":"Friedlander, T., Mayo, A., Tlusty, T., &#38; Alon, U. (2015). Evolution of bow-tie architectures in biology. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055\">https://doi.org/10.1371/journal.pcbi.1004055</a>"},"year":"2015","doi":"10.1371/journal.pcbi.1004055","day":"23","abstract":[{"text":"Bow-tie or hourglass structure is a common architectural feature found in many biological systems. A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers. Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes. Little is known, however, about how bow-tie architectures evolve. Here, we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal. We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed. Mathematically speaking, bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient. The maximal compression possible (the rank of the goal) determines the size of the narrowest part of the network—that is the bow-tie. A further requirement is that a process is active to reduce the number of links in the network, such as product-rule mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations. This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the effective rank of the goals under which they evolved.","lang":"eng"}],"volume":11,"ddc":["576"],"_id":"1827","scopus_import":1,"author":[{"first_name":"Tamar","last_name":"Friedlander","full_name":"Friedlander, Tamar","id":"36A5845C-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Mayo, Avraham","first_name":"Avraham","last_name":"Mayo"},{"full_name":"Tlusty, Tsvi","first_name":"Tsvi","last_name":"Tlusty"},{"first_name":"Uri","last_name":"Alon","full_name":"Alon, Uri"}],"issue":"3","publication_status":"published","date_created":"2018-12-11T11:54:14Z","department":[{"_id":"GaTk"}],"article_processing_charge":"No","pubrep_id":"452","title":"Evolution of bow-tie architectures in biology","intvolume":"        11","quality_controlled":"1","ec_funded":1,"file_date_updated":"2020-07-14T12:45:17Z","publisher":"Public Library of Science"},{"month":"02","title":"Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks","intvolume":"        25","article_number":"8","publication_status":"published","oa_version":"None","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"date_created":"2018-12-11T11:54:25Z","author":[{"full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","last_name":"Ruess","first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Lygeros","first_name":"John","full_name":"Lygeros, John"}],"issue":"2","publication":"ACM Transactions on Modeling and Computer Simulation","_id":"1861","scopus_import":1,"publisher":"ACM","language":[{"iso":"eng"}],"quality_controlled":"1","abstract":[{"lang":"eng","text":"Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of themolecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation that governs the time evolution of the probability distribution of the system. This, however, is rarely possible, and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this article is to base methods on only some low-order moments of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance."}],"publist_id":"5238","doi":"10.1145/2688906","day":"01","date_published":"2015-02-01T00:00:00Z","type":"journal_article","date_updated":"2021-01-12T06:53:41Z","year":"2015","citation":{"ista":"Ruess J, Lygeros J. 2015. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 25(2), 8.","mla":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” <i>ACM Transactions on Modeling and Computer Simulation</i>, vol. 25, no. 2, 8, ACM, 2015, doi:<a href=\"https://doi.org/10.1145/2688906\">10.1145/2688906</a>.","short":"J. Ruess, J. Lygeros, ACM Transactions on Modeling and Computer Simulation 25 (2015).","ieee":"J. Ruess and J. Lygeros, “Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks,” <i>ACM Transactions on Modeling and Computer Simulation</i>, vol. 25, no. 2. ACM, 2015.","chicago":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” <i>ACM Transactions on Modeling and Computer Simulation</i>. ACM, 2015. <a href=\"https://doi.org/10.1145/2688906\">https://doi.org/10.1145/2688906</a>.","ama":"Ruess J, Lygeros J. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. <i>ACM Transactions on Modeling and Computer Simulation</i>. 2015;25(2). doi:<a href=\"https://doi.org/10.1145/2688906\">10.1145/2688906</a>","apa":"Ruess, J., &#38; Lygeros, J. (2015). Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. <i>ACM Transactions on Modeling and Computer Simulation</i>. ACM. <a href=\"https://doi.org/10.1145/2688906\">https://doi.org/10.1145/2688906</a>"},"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","volume":25,"acknowledgement":"HYCON2; EC; European Commission\r\n"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","volume":199,"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1404.5599"}],"abstract":[{"text":"The concept of positional information is central to our understanding of how cells determine their location in a multicellular structure and thereby their developmental fates. Nevertheless, positional information has neither been defined mathematically nor quantified in a principled way. Here we provide an information-theoretic definition in the context of developmental gene expression patterns and examine the features of expression patterns that affect positional information quantitatively. We connect positional information with the concept of positional error and develop tools to directly measure information and error from experimental data. We illustrate our framework for the case of gap gene expression patterns in the early Drosophila embryo and show how information that is distributed among only four genes is sufficient to determine developmental fates with nearly single-cell resolution. Our approach can be generalized to a variety of different model systems; procedures and examples are discussed in detail. ","lang":"eng"}],"oa":1,"publist_id":"5210","doi":"10.1534/genetics.114.171850","day":"01","date_published":"2015-01-01T00:00:00Z","type":"journal_article","date_updated":"2021-01-12T06:53:50Z","year":"2015","citation":{"ama":"Tkačik G, Dubuis J, Petkova M, Gregor T. Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. <i>Genetics</i>. 2015;199(1):39-59. doi:<a href=\"https://doi.org/10.1534/genetics.114.171850\">10.1534/genetics.114.171850</a>","apa":"Tkačik, G., Dubuis, J., Petkova, M., &#38; Gregor, T. (2015). Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. <i>Genetics</i>. Genetics Society of America. <a href=\"https://doi.org/10.1534/genetics.114.171850\">https://doi.org/10.1534/genetics.114.171850</a>","chicago":"Tkačik, Gašper, Julien Dubuis, Mariela Petkova, and Thomas Gregor. “Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework.” <i>Genetics</i>. Genetics Society of America, 2015. <a href=\"https://doi.org/10.1534/genetics.114.171850\">https://doi.org/10.1534/genetics.114.171850</a>.","ieee":"G. Tkačik, J. Dubuis, M. Petkova, and T. Gregor, “Positional information, positional error, and readout precision in morphogenesis: A mathematical framework,” <i>Genetics</i>, vol. 199, no. 1. Genetics Society of America, pp. 39–59, 2015.","mla":"Tkačik, Gašper, et al. “Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework.” <i>Genetics</i>, vol. 199, no. 1, Genetics Society of America, 2015, pp. 39–59, doi:<a href=\"https://doi.org/10.1534/genetics.114.171850\">10.1534/genetics.114.171850</a>.","short":"G. Tkačik, J. Dubuis, M. Petkova, T. Gregor, Genetics 199 (2015) 39–59.","ista":"Tkačik G, Dubuis J, Petkova M, Gregor T. 2015. Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. 199(1), 39–59."},"publisher":"Genetics Society of America","language":[{"iso":"eng"}],"page":"39 - 59","quality_controlled":"1","month":"01","title":"Positional information, positional error, and readout precision in morphogenesis: A mathematical framework","intvolume":"       199","publication_status":"published","oa_version":"Preprint","date_created":"2018-12-11T11:54:32Z","department":[{"_id":"GaTk"}],"author":[{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Dubuis","first_name":"Julien","full_name":"Dubuis, Julien"},{"full_name":"Petkova, Mariela","first_name":"Mariela","last_name":"Petkova"},{"full_name":"Gregor, Thomas","first_name":"Thomas","last_name":"Gregor"}],"issue":"1","_id":"1885","publication":"Genetics","scopus_import":1},{"department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:54:49Z","oa_version":"Preprint","publication_status":"published","intvolume":"        91","article_number":"062710","month":"06","title":"Optimizing information flow in small genetic networks. IV. Spatial coupling","scopus_import":1,"_id":"1940","publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","issue":"6","author":[{"id":"3E999752-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas R","last_name":"Sokolowski","orcid":"0000-0002-1287-3779","full_name":"Sokolowski, Thomas R"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","first_name":"Gasper"}],"publisher":"American Institute of Physics","quality_controlled":"1","language":[{"iso":"eng"}],"day":"15","doi":"10.1103/PhysRevE.91.062710","publist_id":"5145","oa":1,"abstract":[{"text":"We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the &quot;input&quot;) by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively new regulatory strategy emerges where individual cells respond to the input in a nearly step-like fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.","lang":"eng"}],"year":"2015","citation":{"mla":"Sokolowski, Thomas R., and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 91, no. 6, 062710, American Institute of Physics, 2015, doi:<a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">10.1103/PhysRevE.91.062710</a>.","short":"T.R. Sokolowski, G. Tkačik, Physical Review E Statistical Nonlinear and Soft Matter Physics 91 (2015).","ista":"Sokolowski TR, Tkačik G. 2015. Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. 91(6), 062710.","apa":"Sokolowski, T. R., &#38; Tkačik, G. (2015). Optimizing information flow in small genetic networks. IV. Spatial coupling. <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics. <a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">https://doi.org/10.1103/PhysRevE.91.062710</a>","ama":"Sokolowski TR, Tkačik G. Optimizing information flow in small genetic networks. IV. Spatial coupling. <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. 2015;91(6). doi:<a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">10.1103/PhysRevE.91.062710</a>","chicago":"Sokolowski, Thomas R, and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics, 2015. <a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">https://doi.org/10.1103/PhysRevE.91.062710</a>.","ieee":"T. R. Sokolowski and G. Tkačik, “Optimizing information flow in small genetic networks. IV. Spatial coupling,” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 91, no. 6. American Institute of Physics, 2015."},"date_updated":"2021-01-12T06:54:13Z","type":"journal_article","date_published":"2015-06-15T00:00:00Z","main_file_link":[{"url":"http://arxiv.org/abs/1501.04015","open_access":"1"}],"volume":91,"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"ec_funded":1,"quality_controlled":"1","page":"8148 - 8153","publisher":"National Academy of Sciences","scopus_import":1,"_id":"1538","pmid":1,"issue":"26","author":[{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","last_name":"Ruess","first_name":"Jakob","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282"},{"full_name":"Parise, Francesca","first_name":"Francesca","last_name":"Parise"},{"full_name":"Milias Argeitis, Andreas","first_name":"Andreas","last_name":"Milias Argeitis"},{"full_name":"Khammash, Mustafa","last_name":"Khammash","first_name":"Mustafa"},{"full_name":"Lygeros, John","last_name":"Lygeros","first_name":"John"}],"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"date_created":"2018-12-11T11:52:36Z","publication_status":"published","intvolume":"       112","title":"Iterative experiment design guides the characterization of a light-inducible gene expression circuit","acknowledgement":"J.R., F.P., and J.L. acknowledge support from the European Commission under the Network of Excellence HYCON2 (highly-complex and networked control systems) and SystemsX.ch under the SignalX Project. J.R. acknowledges support from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013 under REA (Research Executive Agency) Grant 291734. M.K. acknowledges support from Human Frontier Science Program Grant RP0061/2011 (www.hfsp.org). ","volume":112,"citation":{"short":"J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, J. Lygeros, PNAS 112 (2015) 8148–8153.","mla":"Ruess, Jakob, et al. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” <i>PNAS</i>, vol. 112, no. 26, National Academy of Sciences, 2015, pp. 8148–53, doi:<a href=\"https://doi.org/10.1073/pnas.1423947112\">10.1073/pnas.1423947112</a>.","ista":"Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. 2015. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. 112(26), 8148–8153.","ama":"Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. <i>PNAS</i>. 2015;112(26):8148-8153. doi:<a href=\"https://doi.org/10.1073/pnas.1423947112\">10.1073/pnas.1423947112</a>","apa":"Ruess, J., Parise, F., Milias Argeitis, A., Khammash, M., &#38; Lygeros, J. (2015). Iterative experiment design guides the characterization of a light-inducible gene expression circuit. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1423947112\">https://doi.org/10.1073/pnas.1423947112</a>","ieee":"J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, and J. Lygeros, “Iterative experiment design guides the characterization of a light-inducible gene expression circuit,” <i>PNAS</i>, vol. 112, no. 26. National Academy of Sciences, pp. 8148–8153, 2015.","chicago":"Ruess, Jakob, Francesca Parise, Andreas Milias Argeitis, Mustafa Khammash, and John Lygeros. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” <i>PNAS</i>. National Academy of Sciences, 2015. <a href=\"https://doi.org/10.1073/pnas.1423947112\">https://doi.org/10.1073/pnas.1423947112</a>."},"year":"2015","date_updated":"2021-01-12T06:51:27Z","external_id":{"pmid":["26085136"]},"day":"30","doi":"10.1073/pnas.1423947112","abstract":[{"text":"Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal experiment design, flow cytometry experiments, and Bayesian parameter inference to guide the discovery process in the case of stochastic biochemical reaction networks. To illustrate the benefit of our methodology, we apply it to the characterization of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from nonoptimal experiments. In particular, we compare the parameter posterior distributions and the precision to which the outcome of future experiments can be predicted. Moreover, we illustrate how the identified stochastic model can be used to determine light induction patterns that make either the average amount of protein or the variability in a population of cells follow a desired profile. Our results show that optimal experiment design allows one to derive models that are accurate enough to precisely predict and regulate the protein expression in heterogeneous cell populations over extended periods of time.","lang":"eng"}],"language":[{"iso":"eng"}],"publication":"PNAS","project":[{"grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"oa_version":"Submitted Version","month":"06","main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491780/","open_access":"1"}],"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"journal_article","date_published":"2015-06-30T00:00:00Z","oa":1,"publist_id":"5633"},{"publisher":"American Institute of Physics","file_date_updated":"2020-07-14T12:45:01Z","ec_funded":1,"quality_controlled":"1","intvolume":"       143","pubrep_id":"593","title":"Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space","date_created":"2018-12-11T11:52:36Z","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publication_status":"published","issue":"24","author":[{"orcid":"0000-0003-1615-3282","full_name":"Ruess, Jakob","first_name":"Jakob","last_name":"Ruess","id":"4A245D00-F248-11E8-B48F-1D18A9856A87"}],"scopus_import":1,"_id":"1539","ddc":["000"],"volume":143,"abstract":[{"lang":"eng","text":"Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space. "}],"day":"22","doi":"10.1063/1.4937937","year":"2015","citation":{"apa":"Ruess, J. (2015). Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. <i>Journal of Chemical Physics</i>. American Institute of Physics. <a href=\"https://doi.org/10.1063/1.4937937\">https://doi.org/10.1063/1.4937937</a>","ama":"Ruess J. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. <i>Journal of Chemical Physics</i>. 2015;143(24). doi:<a href=\"https://doi.org/10.1063/1.4937937\">10.1063/1.4937937</a>","ieee":"J. Ruess, “Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space,” <i>Journal of Chemical Physics</i>, vol. 143, no. 24. American Institute of Physics, 2015.","chicago":"Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” <i>Journal of Chemical Physics</i>. American Institute of Physics, 2015. <a href=\"https://doi.org/10.1063/1.4937937\">https://doi.org/10.1063/1.4937937</a>.","short":"J. Ruess, Journal of Chemical Physics 143 (2015).","mla":"Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” <i>Journal of Chemical Physics</i>, vol. 143, no. 24, 244103, American Institute of Physics, 2015, doi:<a href=\"https://doi.org/10.1063/1.4937937\">10.1063/1.4937937</a>.","ista":"Ruess J. 2015. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. 143(24), 244103."},"date_updated":"2021-01-12T06:51:28Z","language":[{"iso":"eng"}],"article_number":"244103","month":"12","project":[{"call_identifier":"FP7","_id":"25EE3708-B435-11E9-9278-68D0E5697425","name":"Quantitative Reactive Modeling","grant_number":"267989"},{"name":"Rigorous Systems Engineering","grant_number":"S 11407_N23","_id":"25832EC2-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211","name":"The Wittgenstein Prize"},{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"oa_version":"Published Version","has_accepted_license":"1","publication":"Journal of Chemical Physics","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","file":[{"access_level":"open_access","relation":"main_file","creator":"system","file_id":"4641","checksum":"838657118ae286463a2b7737319f35ce","file_size":605355,"date_created":"2018-12-12T10:07:43Z","content_type":"application/pdf","file_name":"IST-2016-593-v1+1_Minimal_moment_equations.pdf","date_updated":"2020-07-14T12:45:01Z"}],"publist_id":"5632","oa":1,"type":"journal_article","date_published":"2015-12-22T00:00:00Z"},{"language":[{"iso":"eng"}],"month":"11","article_number":"145","oa_version":"Published Version","project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"}],"publication":"Frontiers in Computational Neuroscience","has_accepted_license":"1","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file":[{"file_name":"IST-2016-479-v1+1_fncom-09-00145.pdf","content_type":"application/pdf","date_updated":"2020-07-14T12:45:02Z","file_size":187038,"checksum":"cea73b6d3ef1579f32da10b82f4de4fd","date_created":"2018-12-12T10:12:09Z","creator":"system","file_id":"4927","relation":"main_file","access_level":"open_access"}],"publist_id":"5607","oa":1,"date_published":"2015-11-30T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"publisher":"Frontiers Research Foundation","file_date_updated":"2020-07-14T12:45:02Z","quality_controlled":"1","ec_funded":1,"pubrep_id":"479","title":"Editorial: Emergent neural computation from the interaction of different forms of plasticity","intvolume":"         9","publication_status":"published","department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:52:45Z","author":[{"first_name":"Matthieu","last_name":"Gilson","full_name":"Gilson, Matthieu"},{"id":"3933349E-F248-11E8-B48F-1D18A9856A87","first_name":"Cristina","last_name":"Savin","full_name":"Savin, Cristina"},{"full_name":"Zenke, Friedemann","last_name":"Zenke","first_name":"Friedemann"}],"issue":"11","_id":"1564","scopus_import":1,"ddc":["570"],"volume":9,"doi":"10.3389/fncom.2015.00145","day":"30","date_updated":"2021-01-12T06:51:37Z","citation":{"ista":"Gilson M, Savin C, Zenke F. 2015. Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. 9(11), 145.","short":"M. Gilson, C. Savin, F. Zenke, Frontiers in Computational Neuroscience 9 (2015).","mla":"Gilson, Matthieu, et al. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” <i>Frontiers in Computational Neuroscience</i>, vol. 9, no. 11, 145, Frontiers Research Foundation, 2015, doi:<a href=\"https://doi.org/10.3389/fncom.2015.00145\">10.3389/fncom.2015.00145</a>.","ieee":"M. Gilson, C. Savin, and F. Zenke, “Editorial: Emergent neural computation from the interaction of different forms of plasticity,” <i>Frontiers in Computational Neuroscience</i>, vol. 9, no. 11. Frontiers Research Foundation, 2015.","chicago":"Gilson, Matthieu, Cristina Savin, and Friedemann Zenke. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” <i>Frontiers in Computational Neuroscience</i>. Frontiers Research Foundation, 2015. <a href=\"https://doi.org/10.3389/fncom.2015.00145\">https://doi.org/10.3389/fncom.2015.00145</a>.","apa":"Gilson, M., Savin, C., &#38; Zenke, F. (2015). Editorial: Emergent neural computation from the interaction of different forms of plasticity. <i>Frontiers in Computational Neuroscience</i>. Frontiers Research Foundation. <a href=\"https://doi.org/10.3389/fncom.2015.00145\">https://doi.org/10.3389/fncom.2015.00145</a>","ama":"Gilson M, Savin C, Zenke F. Editorial: Emergent neural computation from the interaction of different forms of plasticity. <i>Frontiers in Computational Neuroscience</i>. 2015;9(11). doi:<a href=\"https://doi.org/10.3389/fncom.2015.00145\">10.3389/fncom.2015.00145</a>"},"year":"2015"},{"volume":112,"citation":{"ista":"Der R, Martius GS. 2015. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.","short":"R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.","mla":"Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” <i>PNAS</i>, vol. 112, no. 45, National Academy of Sciences, 2015, pp. E6224–32, doi:<a href=\"https://doi.org/10.1073/pnas.1508400112\">10.1073/pnas.1508400112</a>.","chicago":"Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” <i>PNAS</i>. National Academy of Sciences, 2015. <a href=\"https://doi.org/10.1073/pnas.1508400112\">https://doi.org/10.1073/pnas.1508400112</a>.","ieee":"R. Der and G. S. Martius, “Novel plasticity rule can explain the development of sensorimotor intelligence,” <i>PNAS</i>, vol. 112, no. 45. National Academy of Sciences, pp. E6224–E6232, 2015.","ama":"Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor intelligence. <i>PNAS</i>. 2015;112(45):E6224-E6232. doi:<a href=\"https://doi.org/10.1073/pnas.1508400112\">10.1073/pnas.1508400112</a>","apa":"Der, R., &#38; Martius, G. S. (2015). Novel plasticity rule can explain the development of sensorimotor intelligence. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1508400112\">https://doi.org/10.1073/pnas.1508400112</a>"},"year":"2015","date_updated":"2021-01-12T06:51:40Z","external_id":{"pmid":["26504200"]},"day":"10","doi":"10.1073/pnas.1508400112","abstract":[{"lang":"eng","text":"Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher-level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no systemspecific modifications of the DEP rule. They rather arise from the underlying mechanism of spontaneous symmetry breaking,which is due to the tight brain body environment coupling. The new synaptic rule is biologically plausible and would be an interesting target for neurobiological investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution."}],"quality_controlled":"1","ec_funded":1,"page":"E6224 - E6232","publisher":"National Academy of Sciences","scopus_import":1,"_id":"1570","pmid":1,"issue":"45","author":[{"full_name":"Der, Ralf","last_name":"Der","first_name":"Ralf"},{"last_name":"Martius","first_name":"Georg S","full_name":"Martius, Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87"}],"date_created":"2018-12-11T11:52:47Z","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"publication_status":"published","intvolume":"       112","title":"Novel plasticity rule can explain the development of sensorimotor intelligence","main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/","open_access":"1"}],"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"journal_article","date_published":"2015-11-10T00:00:00Z","publist_id":"5601","oa":1,"language":[{"iso":"eng"}],"publication":"PNAS","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"oa_version":"Submitted Version","month":"11"},{"ec_funded":1,"quality_controlled":"1","publisher":"American Physical Society","_id":"1576","scopus_import":1,"author":[{"id":"3DEE19A4-F248-11E8-B48F-1D18A9856A87","full_name":"Cepeda Humerez, Sarah A","first_name":"Sarah A","last_name":"Cepeda Humerez"},{"id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","last_name":"Rieckh","first_name":"Georg","full_name":"Rieckh, Georg"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","first_name":"Gasper","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455"}],"issue":"24","publication_status":"published","department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:52:49Z","title":"Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation","intvolume":"       115","volume":115,"date_updated":"2023-09-07T12:55:21Z","year":"2015","citation":{"chicago":"Cepeda Humerez, Sarah A, Georg Rieckh, and Gašper Tkačik. “Stochastic Proofreading Mechanism Alleviates Crosstalk in Transcriptional Regulation.” <i>Physical Review Letters</i>. American Physical Society, 2015. <a href=\"https://doi.org/10.1103/PhysRevLett.115.248101\">https://doi.org/10.1103/PhysRevLett.115.248101</a>.","ieee":"S. A. Cepeda Humerez, G. Rieckh, and G. Tkačik, “Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation,” <i>Physical Review Letters</i>, vol. 115, no. 24. American Physical Society, 2015.","ama":"Cepeda Humerez SA, Rieckh G, Tkačik G. Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation. <i>Physical Review Letters</i>. 2015;115(24). doi:<a href=\"https://doi.org/10.1103/PhysRevLett.115.248101\">10.1103/PhysRevLett.115.248101</a>","apa":"Cepeda Humerez, S. A., Rieckh, G., &#38; Tkačik, G. (2015). Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation. <i>Physical Review Letters</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevLett.115.248101\">https://doi.org/10.1103/PhysRevLett.115.248101</a>","ista":"Cepeda Humerez SA, Rieckh G, Tkačik G. 2015. Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation. Physical Review Letters. 115(24), 248101.","mla":"Cepeda Humerez, Sarah A., et al. “Stochastic Proofreading Mechanism Alleviates Crosstalk in Transcriptional Regulation.” <i>Physical Review Letters</i>, vol. 115, no. 24, 248101, American Physical Society, 2015, doi:<a href=\"https://doi.org/10.1103/PhysRevLett.115.248101\">10.1103/PhysRevLett.115.248101</a>.","short":"S.A. Cepeda Humerez, G. Rieckh, G. Tkačik, Physical Review Letters 115 (2015)."},"doi":"10.1103/PhysRevLett.115.248101","day":"08","abstract":[{"lang":"eng","text":"Gene expression is controlled primarily by interactions between transcription factor proteins (TFs) and the regulatory DNA sequence, a process that can be captured well by thermodynamic models of regulation. These models, however, neglect regulatory crosstalk: the possibility that noncognate TFs could initiate transcription, with potentially disastrous effects for the cell. Here, we estimate the importance of crosstalk, suggest that its avoidance strongly constrains equilibrium models of TF binding, and propose an alternative nonequilibrium scheme that implements kinetic proofreading to suppress erroneous initiation. This proposal is consistent with the observed covalent modifications of the transcriptional apparatus and predicts increased noise in gene expression as a trade-off for improved specificity. Using information theory, we quantify this trade-off to find when optimal proofreading architectures are favored over their equilibrium counterparts. Such architectures exhibit significant super-Poisson noise at low expression in steady state."}],"language":[{"iso":"eng"}],"publication":"Physical Review Letters","oa_version":"Preprint","project":[{"grant_number":"250152","name":"Limits to selection in biology and in evolutionary computation","_id":"25B07788-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"month":"12","article_number":"248101","main_file_link":[{"url":"http://arxiv.org/abs/1504.05716","open_access":"1"}],"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","related_material":{"record":[{"id":"6473","relation":"part_of_dissertation","status":"public"}]},"date_published":"2015-12-08T00:00:00Z","type":"journal_article","publist_id":"5595","oa":1},{"citation":{"ama":"Tugrul M, Paixao T, Barton NH, Tkačik G. Other fitness models for comparison &#38; for interacting TFBSs. 2015. doi:<a href=\"https://doi.org/10.1371/journal.pgen.1005639.s001\">10.1371/journal.pgen.1005639.s001</a>","apa":"Tugrul, M., Paixao, T., Barton, N. H., &#38; Tkačik, G. (2015). Other fitness models for comparison &#38; for interacting TFBSs. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pgen.1005639.s001\">https://doi.org/10.1371/journal.pgen.1005639.s001</a>","chicago":"Tugrul, Murat, Tiago Paixao, Nicholas H Barton, and Gašper Tkačik. “Other Fitness Models for Comparison &#38; for Interacting TFBSs.” Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pgen.1005639.s001\">https://doi.org/10.1371/journal.pgen.1005639.s001</a>.","ieee":"M. Tugrul, T. Paixao, N. H. Barton, and G. Tkačik, “Other fitness models for comparison &#38; for interacting TFBSs.” Public Library of Science, 2015.","mla":"Tugrul, Murat, et al. <i>Other Fitness Models for Comparison &#38; for Interacting TFBSs</i>. Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pgen.1005639.s001\">10.1371/journal.pgen.1005639.s001</a>.","short":"M. Tugrul, T. Paixao, N.H. Barton, G. Tkačik, (2015).","ista":"Tugrul M, Paixao T, Barton NH, Tkačik G. 2015. Other fitness models for comparison &#38; for interacting TFBSs, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pgen.1005639.s001\">10.1371/journal.pgen.1005639.s001</a>."},"year":"2015","date_updated":"2025-05-28T11:57:04Z","type":"research_data_reference","date_published":"2015-11-06T00:00:00Z","day":"06","doi":"10.1371/journal.pgen.1005639.s001","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","status":"public","related_material":{"record":[{"relation":"used_in_publication","id":"1666","status":"public"}]},"_id":"9712","author":[{"id":"37C323C6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8523-0758","full_name":"Tugrul, Murat","first_name":"Murat","last_name":"Tugrul"},{"last_name":"Paixao","first_name":"Tiago","full_name":"Paixao, Tiago","orcid":"0000-0003-2361-3953","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Barton","first_name":"Nicholas H","full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"department":[{"_id":"NiBa"},{"_id":"CaGu"},{"_id":"GaTk"}],"date_created":"2021-07-23T12:00:37Z","article_processing_charge":"No","oa_version":"Published Version","title":"Other fitness models for comparison & for interacting TFBSs","month":"11","publisher":"Public Library of Science"},{"publisher":"Public Library of Science","_id":"9718","author":[{"id":"36A5845C-F248-11E8-B48F-1D18A9856A87","first_name":"Tamar","last_name":"Friedlander","full_name":"Friedlander, Tamar"},{"first_name":"Avraham E.","last_name":"Mayo","full_name":"Mayo, Avraham E."},{"full_name":"Tlusty, Tsvi","last_name":"Tlusty","first_name":"Tsvi"},{"first_name":"Uri","last_name":"Alon","full_name":"Alon, Uri"}],"oa_version":"Published Version","department":[{"_id":"GaTk"}],"article_processing_charge":"No","date_created":"2021-07-26T08:35:23Z","title":"Supporting information text","month":"03","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"1827"}]},"status":"public","date_updated":"2023-02-23T10:16:13Z","citation":{"ieee":"T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Supporting information text.” Public Library of Science, 2015.","chicago":"Friedlander, Tamar, Avraham E. Mayo, Tsvi Tlusty, and Uri Alon. “Supporting Information Text.” Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s001\">https://doi.org/10.1371/journal.pcbi.1004055.s001</a>.","ama":"Friedlander T, Mayo AE, Tlusty T, Alon U. Supporting information text. 2015. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s001\">10.1371/journal.pcbi.1004055.s001</a>","apa":"Friedlander, T., Mayo, A. E., Tlusty, T., &#38; Alon, U. (2015). Supporting information text. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s001\">https://doi.org/10.1371/journal.pcbi.1004055.s001</a>","ista":"Friedlander T, Mayo AE, Tlusty T, Alon U. 2015. Supporting information text, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s001\">10.1371/journal.pcbi.1004055.s001</a>.","short":"T. Friedlander, A.E. Mayo, T. Tlusty, U. Alon, (2015).","mla":"Friedlander, Tamar, et al. <i>Supporting Information Text</i>. Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s001\">10.1371/journal.pcbi.1004055.s001</a>."},"year":"2015","date_published":"2015-03-23T00:00:00Z","type":"research_data_reference","doi":"10.1371/journal.pcbi.1004055.s001","day":"23"},{"status":"public","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","related_material":{"record":[{"relation":"used_in_publication","id":"1827","status":"public"}]},"day":"23","doi":"10.1371/journal.pcbi.1004055.s002","citation":{"chicago":"Friedlander, Tamar, Avraham E. Mayo, Tsvi Tlusty, and Uri Alon. “Evolutionary Simulation Code.” Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s002\">https://doi.org/10.1371/journal.pcbi.1004055.s002</a>.","ieee":"T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Evolutionary simulation code.” Public Library of Science, 2015.","apa":"Friedlander, T., Mayo, A. E., Tlusty, T., &#38; Alon, U. (2015). Evolutionary simulation code. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s002\">https://doi.org/10.1371/journal.pcbi.1004055.s002</a>","ama":"Friedlander T, Mayo AE, Tlusty T, Alon U. Evolutionary simulation code. 2015. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s002\">10.1371/journal.pcbi.1004055.s002</a>","ista":"Friedlander T, Mayo AE, Tlusty T, Alon U. 2015. Evolutionary simulation code, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s002\">10.1371/journal.pcbi.1004055.s002</a>.","mla":"Friedlander, Tamar, et al. <i>Evolutionary Simulation Code</i>. Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055.s002\">10.1371/journal.pcbi.1004055.s002</a>.","short":"T. Friedlander, A.E. Mayo, T. Tlusty, U. Alon, (2015)."},"year":"2015","date_updated":"2023-02-23T10:16:13Z","type":"research_data_reference","date_published":"2015-03-23T00:00:00Z","publisher":"Public Library of Science","date_created":"2021-08-05T12:58:07Z","article_processing_charge":"No","department":[{"_id":"GaTk"}],"oa_version":"Published Version","month":"03","title":"Evolutionary simulation code","_id":"9773","author":[{"id":"36A5845C-F248-11E8-B48F-1D18A9856A87","last_name":"Friedlander","first_name":"Tamar","full_name":"Friedlander, Tamar"},{"first_name":"Avraham E.","last_name":"Mayo","full_name":"Mayo, Avraham E."},{"full_name":"Tlusty, Tsvi","first_name":"Tsvi","last_name":"Tlusty"},{"full_name":"Alon, Uri","first_name":"Uri","last_name":"Alon"}]},{"user_id":"3FFCCD3A-F248-11E8-B48F-1D18A9856A87","status":"public","file":[{"date_created":"2018-12-12T10:13:28Z","checksum":"1d5816b343abe5eadc3eb419bcece971","file_size":1568524,"date_updated":"2020-07-14T12:46:06Z","content_type":"application/pdf","file_name":"IST-2016-432-v1+1_journal.pone.0085841.pdf","access_level":"open_access","relation":"main_file","file_id":"5011","creator":"system"}],"oa":1,"publist_id":"3385","date_published":"2014-01-21T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"language":[{"iso":"eng"}],"month":"01","article_number":"e85841","oa_version":"Published Version","publication":"PLoS One","has_accepted_license":"1","ddc":["570"],"volume":9,"acknowledgement":"This work was supported by The Israel Science Foundation and The Human Frontiers Science Program.\r\nWe thank the referees for helping significantly improve this paper. We also thank Vijay Balasubramanian, Kristina Simmons, and Jason Prentice for stimulating discussions. GT wishes to thank the faculty and students of the “Methods in Computational Neuroscience” course at Marine Biological Laboratory, Woods Hole.\r\n","abstract":[{"text":"Adaptation in the retina is thought to optimize the encoding of natural light signals into sequences of spikes sent to the brain. While adaptive changes in retinal processing to the variations of the mean luminance level and second-order stimulus statistics have been documented before, no such measurements have been performed when higher-order moments of the light distribution change. We therefore measured the ganglion cell responses in the tiger salamander retina to controlled changes in the second (contrast), third (skew) and fourth (kurtosis) moments of the light intensity distribution of spatially uniform temporally independent stimuli. The skew and kurtosis of the stimuli were chosen to cover the range observed in natural scenes. We quantified adaptation in ganglion cells by studying linear-nonlinear models that capture well the retinal encoding properties across all stimuli. We found that the encoding properties of retinal ganglion cells change only marginally when higher-order statistics change, compared to the changes observed in response to the variation in contrast. By analyzing optimal coding in LN-type models, we showed that neurons can maintain a high information rate without large dynamic adaptation to changes in skew or kurtosis. This is because, for uncorrelated stimuli, spatio-temporal summation within the receptive field averages away non-gaussian aspects of the light intensity distribution.","lang":"eng"}],"doi":"10.1371/journal.pone.0085841","day":"21","date_updated":"2021-01-12T07:42:14Z","citation":{"chicago":"Tkačik, Gašper, Anandamohan Ghosh, Elad Schneidman, and Ronen Segev. “Adaptation to Changes in Higher-Order Stimulus Statistics in the Salamander Retina.” <i>PLoS One</i>. Public Library of Science, 2014. <a href=\"https://doi.org/10.1371/journal.pone.0085841\">https://doi.org/10.1371/journal.pone.0085841</a>.","ieee":"G. Tkačik, A. Ghosh, E. Schneidman, and R. Segev, “Adaptation to changes in higher-order stimulus statistics in the salamander retina,” <i>PLoS One</i>, vol. 9, no. 1. Public Library of Science, 2014.","ama":"Tkačik G, Ghosh A, Schneidman E, Segev R. Adaptation to changes in higher-order stimulus statistics in the salamander retina. <i>PLoS One</i>. 2014;9(1). doi:<a href=\"https://doi.org/10.1371/journal.pone.0085841\">10.1371/journal.pone.0085841</a>","apa":"Tkačik, G., Ghosh, A., Schneidman, E., &#38; Segev, R. (2014). Adaptation to changes in higher-order stimulus statistics in the salamander retina. <i>PLoS One</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0085841\">https://doi.org/10.1371/journal.pone.0085841</a>","ista":"Tkačik G, Ghosh A, Schneidman E, Segev R. 2014. Adaptation to changes in higher-order stimulus statistics in the salamander retina. PLoS One. 9(1), e85841.","mla":"Tkačik, Gašper, et al. “Adaptation to Changes in Higher-Order Stimulus Statistics in the Salamander Retina.” <i>PLoS One</i>, vol. 9, no. 1, e85841, Public Library of Science, 2014, doi:<a href=\"https://doi.org/10.1371/journal.pone.0085841\">10.1371/journal.pone.0085841</a>.","short":"G. Tkačik, A. Ghosh, E. Schneidman, R. Segev, PLoS One 9 (2014)."},"year":"2014","publisher":"Public Library of Science","file_date_updated":"2020-07-14T12:46:06Z","quality_controlled":"1","pubrep_id":"432","title":"Adaptation to changes in higher-order stimulus statistics in the salamander retina","intvolume":"         9","publication_status":"published","date_created":"2018-12-11T12:02:20Z","department":[{"_id":"GaTk"}],"author":[{"last_name":"Tkacik","first_name":"Gasper","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Anandamohan","last_name":"Ghosh","full_name":"Ghosh, Anandamohan"},{"full_name":"Schneidman, Elad","first_name":"Elad","last_name":"Schneidman"},{"full_name":"Segev, Ronen","first_name":"Ronen","last_name":"Segev"}],"issue":"1","_id":"3263","scopus_import":1},{"page":"2024 - 2032","quality_controlled":"1","language":[{"iso":"eng"}],"publisher":"Neural Information Processing Systems","conference":{"start_date":"2014-12-08","name":"NIPS: Neural Information Processing Systems","end_date":"2014-12-13","location":"Montreal, Canada"},"_id":"1708","scopus_import":1,"author":[{"id":"3933349E-F248-11E8-B48F-1D18A9856A87","first_name":"Cristina","last_name":"Savin","full_name":"Savin, Cristina"},{"full_name":"Denève, Sophie","last_name":"Denève","first_name":"Sophie"}],"issue":"January","oa_version":"None","publication_status":"published","date_created":"2018-12-11T11:53:35Z","department":[{"_id":"GaTk"}],"month":"01","title":"Spatio-temporal representations of uncertainty in spiking neural networks","intvolume":"         3","volume":3,"main_file_link":[{"url":"http://papers.nips.cc/paper/5343-spatio-temporal-representations-of-uncertainty-in-spiking-neural-networks.pdf"}],"status":"public","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","date_updated":"2021-01-12T06:52:40Z","citation":{"ista":"Savin C, Denève S. 2014. Spatio-temporal representations of uncertainty in spiking neural networks. NIPS: Neural Information Processing Systems vol. 3, 2024–2032.","mla":"Savin, Cristina, and Sophie Denève. <i>Spatio-Temporal Representations of Uncertainty in Spiking Neural Networks</i>. Vol. 3, no. January, Neural Information Processing Systems, 2014, pp. 2024–32.","short":"C. Savin, S. Denève, in:, Neural Information Processing Systems, 2014, pp. 2024–2032.","ieee":"C. Savin and S. Denève, “Spatio-temporal representations of uncertainty in spiking neural networks,” presented at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2014, vol. 3, no. January, pp. 2024–2032.","chicago":"Savin, Cristina, and Sophie Denève. “Spatio-Temporal Representations of Uncertainty in Spiking Neural Networks,” 3:2024–32. Neural Information Processing Systems, 2014.","apa":"Savin, C., &#38; Denève, S. (2014). Spatio-temporal representations of uncertainty in spiking neural networks (Vol. 3, pp. 2024–2032). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems.","ama":"Savin C, Denève S. Spatio-temporal representations of uncertainty in spiking neural networks. In: Vol 3. Neural Information Processing Systems; 2014:2024-2032."},"year":"2014","date_published":"2014-01-01T00:00:00Z","type":"conference","day":"01","abstract":[{"lang":"eng","text":"It has been long argued that, because of inherent ambiguity and noise, the brain needs to represent uncertainty in the form of probability distributions. The neural encoding of such distributions remains however highly controversial. Here we present a novel circuit model for representing multidimensional real-valued distributions using a spike based spatio-temporal code. Our model combines the computational advantages of the currently competing models for probabilistic codes and exhibits realistic neural responses along a variety of classic measures. Furthermore, the model highlights the challenges associated with interpreting neural activity in relation to behavioral uncertainty and points to alternative population-level approaches for the experimental validation of distributed representations."}],"publist_id":"5427"},{"abstract":[{"text":"Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime—when sampling limitations constrain performance—efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.","lang":"eng"}],"doi":"10.7554/eLife.03722","day":"14","date_updated":"2021-01-12T06:53:50Z","citation":{"ieee":"A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, and G. Tkačik, “Variance predicts salience in central sensory processing,” <i>eLife</i>, no. November. eLife Sciences Publications, 2014.","chicago":"Hermundstad, Ann, John Briguglio, Mary Conte, Jonathan Victor, Vijay Balasubramanian, and Gašper Tkačik. “Variance Predicts Salience in Central Sensory Processing.” <i>ELife</i>. eLife Sciences Publications, 2014. <a href=\"https://doi.org/10.7554/eLife.03722\">https://doi.org/10.7554/eLife.03722</a>.","apa":"Hermundstad, A., Briguglio, J., Conte, M., Victor, J., Balasubramanian, V., &#38; Tkačik, G. (2014). Variance predicts salience in central sensory processing. <i>ELife</i>. eLife Sciences Publications. <a href=\"https://doi.org/10.7554/eLife.03722\">https://doi.org/10.7554/eLife.03722</a>","ama":"Hermundstad A, Briguglio J, Conte M, Victor J, Balasubramanian V, Tkačik G. Variance predicts salience in central sensory processing. <i>eLife</i>. 2014;(November). doi:<a href=\"https://doi.org/10.7554/eLife.03722\">10.7554/eLife.03722</a>","ista":"Hermundstad A, Briguglio J, Conte M, Victor J, Balasubramanian V, Tkačik G. 2014. Variance predicts salience in central sensory processing. eLife. (November), e03722.","mla":"Hermundstad, Ann, et al. “Variance Predicts Salience in Central Sensory Processing.” <i>ELife</i>, no. November, e03722, eLife Sciences Publications, 2014, doi:<a href=\"https://doi.org/10.7554/eLife.03722\">10.7554/eLife.03722</a>.","short":"A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, G. Tkačik, ELife (2014)."},"year":"2014","ddc":["570"],"pubrep_id":"420","title":"Variance predicts salience in central sensory processing","publication_status":"published","department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:54:32Z","author":[{"full_name":"Hermundstad, Ann","first_name":"Ann","last_name":"Hermundstad"},{"last_name":"Briguglio","first_name":"John","full_name":"Briguglio, John"},{"full_name":"Conte, Mary","first_name":"Mary","last_name":"Conte"},{"full_name":"Victor, Jonathan","last_name":"Victor","first_name":"Jonathan"},{"first_name":"Vijay","last_name":"Balasubramanian","full_name":"Balasubramanian, Vijay"},{"last_name":"Tkacik","first_name":"Gasper","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"issue":"November","_id":"1886","scopus_import":1,"publisher":"eLife Sciences Publications","file_date_updated":"2020-07-14T12:45:20Z","quality_controlled":"1","oa":1,"publist_id":"5209","date_published":"2014-11-14T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"status":"public","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","file":[{"creator":"system","file_id":"4922","relation":"main_file","access_level":"open_access","content_type":"application/pdf","file_name":"IST-2016-420-v1+1_e03722.full.pdf","date_updated":"2020-07-14T12:45:20Z","file_size":5117086,"checksum":"766ac8999ac6e3364f10065a06024b8f","date_created":"2018-12-12T10:12:04Z"}],"month":"11","article_number":"e03722","oa_version":"Published Version","project":[{"_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26"}],"publication":"eLife","has_accepted_license":"1","language":[{"iso":"eng"}]},{"abstract":[{"text":"Biopolymer length regulation is a complex process that involves a large number of biological, chemical, and physical subprocesses acting simultaneously across multiple spatial and temporal scales. An illustrative example important for genomic stability is the length regulation of telomeres - nucleoprotein structures at the ends of linear chromosomes consisting of tandemly repeated DNA sequences and a specialized set of proteins. Maintenance of telomeres is often facilitated by the enzyme telomerase but, particularly in telomerase-free systems, the maintenance of chromosomal termini depends on alternative lengthening of telomeres (ALT) mechanisms mediated by recombination. Various linear and circular DNA structures were identified to participate in ALT, however, dynamics of the whole process is still poorly understood. We propose a chemical kinetics model of ALT with kinetic rates systematically derived from the biophysics of DNA diffusion and looping. The reaction system is reduced to a coagulation-fragmentation system by quasi-steady-state approximation. The detailed treatment of kinetic rates yields explicit formulas for expected size distributions of telomeres that demonstrate the key role played by the J factor, a quantitative measure of bending of polymers. The results are in agreement with experimental data and point out interesting phenomena: an appearance of very long telomeric circles if the total telomere density exceeds a critical value (excess mass) and a nonlinear response of the telomere size distributions to the amount of telomeric DNA in the system. The results can be of general importance for understanding dynamics of telomeres in telomerase-independent systems as this mode of telomere maintenance is similar to the situation in tumor cells lacking telomerase activity. Furthermore, due to its universality, the model may also serve as a prototype of an interaction between linear and circular DNA structures in various settings.","lang":"eng"}],"doi":"10.1103/PhysRevE.89.032701","day":"04","date_updated":"2022-08-01T10:50:10Z","year":"2014","citation":{"ama":"Kollár R, Bodova K, Nosek J, Tomáška Ľ. Mathematical model of alternative mechanism of telomere length maintenance. <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. 2014;89(3). doi:<a href=\"https://doi.org/10.1103/PhysRevE.89.032701\">10.1103/PhysRevE.89.032701</a>","apa":"Kollár, R., Bodova, K., Nosek, J., &#38; Tomáška, Ľ. (2014). Mathematical model of alternative mechanism of telomere length maintenance. <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics. <a href=\"https://doi.org/10.1103/PhysRevE.89.032701\">https://doi.org/10.1103/PhysRevE.89.032701</a>","ieee":"R. Kollár, K. Bodova, J. Nosek, and Ľ. Tomáška, “Mathematical model of alternative mechanism of telomere length maintenance,” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 89, no. 3. American Institute of Physics, 2014.","chicago":"Kollár, Richard, Katarina Bodova, Jozef Nosek, and Ľubomír Tomáška. “Mathematical Model of Alternative Mechanism of Telomere Length Maintenance.” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics, 2014. <a href=\"https://doi.org/10.1103/PhysRevE.89.032701\">https://doi.org/10.1103/PhysRevE.89.032701</a>.","short":"R. Kollár, K. Bodova, J. Nosek, Ľ. Tomáška, Physical Review E Statistical Nonlinear and Soft Matter Physics 89 (2014).","mla":"Kollár, Richard, et al. “Mathematical Model of Alternative Mechanism of Telomere Length Maintenance.” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 89, no. 3, 032701, American Institute of Physics, 2014, doi:<a href=\"https://doi.org/10.1103/PhysRevE.89.032701\">10.1103/PhysRevE.89.032701</a>.","ista":"Kollár R, Bodova K, Nosek J, Tomáška Ľ. 2014. Mathematical model of alternative mechanism of telomere length maintenance. Physical Review E Statistical Nonlinear and Soft Matter Physics. 89(3), 032701."},"acknowledgement":"The work was supported by the VEGA Grant No. 1/0459/13 (R.K. and K.B.).","volume":89,"title":"Mathematical model of alternative mechanism of telomere length maintenance","intvolume":"        89","publication_status":"published","article_processing_charge":"No","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"date_created":"2018-12-11T11:54:35Z","author":[{"first_name":"Richard","last_name":"Kollár","full_name":"Kollár, Richard"},{"orcid":"0000-0002-7214-0171","full_name":"Bod'ová, Katarína","first_name":"Katarína","last_name":"Bod'ová","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Nosek, Jozef","last_name":"Nosek","first_name":"Jozef"},{"first_name":"Ľubomír","last_name":"Tomáška","full_name":"Tomáška, Ľubomír"}],"issue":"3","_id":"1896","scopus_import":"1","publisher":"American Institute of Physics","oa":1,"publist_id":"5198","date_published":"2014-03-04T00:00:00Z","type":"journal_article","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"url":"http://arxiv.org/abs/1402.0430","open_access":"1"}],"month":"03","article_number":"032701","oa_version":"Submitted Version","publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","language":[{"iso":"eng"}]},{"_id":"1909","scopus_import":1,"author":[{"last_name":"Ezard","first_name":"Thomas","full_name":"Ezard, Thomas"},{"full_name":"Prizak, Roshan","last_name":"Prizak","first_name":"Roshan","id":"4456104E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Hoyle","first_name":"Rebecca","full_name":"Hoyle, Rebecca"}],"issue":"3","publication_status":"published","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"date_created":"2018-12-11T11:54:40Z","title":"The fitness costs of adaptation via phenotypic plasticity and maternal effects","pubrep_id":"419","intvolume":"        28","page":"693 - 701","file_date_updated":"2020-07-14T12:45:20Z","publisher":"Wiley-Blackwell","date_updated":"2021-01-12T06:54:00Z","year":"2014","citation":{"short":"T. Ezard, R. Prizak, R. Hoyle, Functional Ecology 28 (2014) 693–701.","mla":"Ezard, Thomas, et al. “The Fitness Costs of Adaptation via Phenotypic Plasticity and Maternal Effects.” <i>Functional Ecology</i>, vol. 28, no. 3, Wiley-Blackwell, 2014, pp. 693–701, doi:<a href=\"https://doi.org/10.1111/1365-2435.12207\">10.1111/1365-2435.12207</a>.","ista":"Ezard T, Prizak R, Hoyle R. 2014. The fitness costs of adaptation via phenotypic plasticity and maternal effects. Functional Ecology. 28(3), 693–701.","apa":"Ezard, T., Prizak, R., &#38; Hoyle, R. (2014). The fitness costs of adaptation via phenotypic plasticity and maternal effects. <i>Functional Ecology</i>. Wiley-Blackwell. <a href=\"https://doi.org/10.1111/1365-2435.12207\">https://doi.org/10.1111/1365-2435.12207</a>","ama":"Ezard T, Prizak R, Hoyle R. The fitness costs of adaptation via phenotypic plasticity and maternal effects. <i>Functional Ecology</i>. 2014;28(3):693-701. doi:<a href=\"https://doi.org/10.1111/1365-2435.12207\">10.1111/1365-2435.12207</a>","chicago":"Ezard, Thomas, Roshan Prizak, and Rebecca Hoyle. “The Fitness Costs of Adaptation via Phenotypic Plasticity and Maternal Effects.” <i>Functional Ecology</i>. Wiley-Blackwell, 2014. <a href=\"https://doi.org/10.1111/1365-2435.12207\">https://doi.org/10.1111/1365-2435.12207</a>.","ieee":"T. Ezard, R. Prizak, and R. Hoyle, “The fitness costs of adaptation via phenotypic plasticity and maternal effects,” <i>Functional Ecology</i>, vol. 28, no. 3. Wiley-Blackwell, pp. 693–701, 2014."},"doi":"10.1111/1365-2435.12207","day":"01","abstract":[{"text":"Summary: Phenotypes are often environmentally dependent, which requires organisms to track environmental change. The challenge for organisms is to construct phenotypes using the most accurate environmental cue. Here, we use a quantitative genetic model of adaptation by additive genetic variance, within- and transgenerational plasticity via linear reaction norms and indirect genetic effects respectively. We show how the relative influence on the eventual phenotype of these components depends on the predictability of environmental change (fast or slow, sinusoidal or stochastic) and the developmental lag τ between when the environment is perceived and when selection acts. We then decompose expected mean fitness into three components (variance load, adaptation and fluctuation load) to study the fitness costs of within- and transgenerational plasticity. A strongly negative maternal effect coefficient m minimizes the variance load, but a strongly positive m minimises the fluctuation load. The adaptation term is maximized closer to zero, with positive or negative m preferred under different environmental scenarios. Phenotypic plasticity is higher when τ is shorter and when the environment changes frequently between seasonal extremes. Expected mean population fitness is highest away from highest observed levels of phenotypic plasticity. Within- and transgenerational plasticity act in concert to deliver well-adapted phenotypes, which emphasizes the need to study both simultaneously when investigating phenotypic evolution.","lang":"eng"}],"acknowledgement":"Engineering and Physical Sciences Research Council. Grant Number: EP/H031928/1","volume":28,"ddc":["570"],"publication":"Functional Ecology","has_accepted_license":"1","oa_version":"Published Version","month":"06","language":[{"iso":"eng"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_published":"2014-06-01T00:00:00Z","type":"journal_article","oa":1,"publist_id":"5186","file":[{"file_id":"5167","creator":"system","relation":"main_file","access_level":"open_access","date_updated":"2020-07-14T12:45:20Z","content_type":"application/pdf","file_name":"IST-2016-419-v1+1_Ezard_et_al-2014-Functional_Ecology.pdf","date_created":"2018-12-12T10:15:45Z","file_size":536154,"checksum":"3cbe8623174709a8ceec2103246f8fe0"}],"user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","status":"public"},{"date_updated":"2021-01-12T06:54:08Z","year":"2014","citation":{"mla":"Humplik, Jan, et al. “Evolutionary Dynamics of Infectious Diseases in Finite Populations.” <i>Journal of Theoretical Biology</i>, vol. 360, Elsevier, 2014, pp. 149–62, doi:<a href=\"https://doi.org/10.1016/j.jtbi.2014.06.039\">10.1016/j.jtbi.2014.06.039</a>.","short":"J. Humplik, A. Hill, M. Nowak, Journal of Theoretical Biology 360 (2014) 149–162.","ista":"Humplik J, Hill A, Nowak M. 2014. Evolutionary dynamics of infectious diseases in finite populations. Journal of Theoretical Biology. 360, 149–162.","ama":"Humplik J, Hill A, Nowak M. Evolutionary dynamics of infectious diseases in finite populations. <i>Journal of Theoretical Biology</i>. 2014;360:149-162. doi:<a href=\"https://doi.org/10.1016/j.jtbi.2014.06.039\">10.1016/j.jtbi.2014.06.039</a>","apa":"Humplik, J., Hill, A., &#38; Nowak, M. (2014). Evolutionary dynamics of infectious diseases in finite populations. <i>Journal of Theoretical Biology</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.jtbi.2014.06.039\">https://doi.org/10.1016/j.jtbi.2014.06.039</a>","chicago":"Humplik, Jan, Alison Hill, and Martin Nowak. “Evolutionary Dynamics of Infectious Diseases in Finite Populations.” <i>Journal of Theoretical Biology</i>. Elsevier, 2014. <a href=\"https://doi.org/10.1016/j.jtbi.2014.06.039\">https://doi.org/10.1016/j.jtbi.2014.06.039</a>.","ieee":"J. Humplik, A. Hill, and M. Nowak, “Evolutionary dynamics of infectious diseases in finite populations,” <i>Journal of Theoretical Biology</i>, vol. 360. Elsevier, pp. 149–162, 2014."},"date_published":"2014-11-07T00:00:00Z","type":"journal_article","doi":"10.1016/j.jtbi.2014.06.039","day":"07","abstract":[{"lang":"eng","text":"In infectious disease epidemiology the basic reproductive ratio, R0, is defined as the average number of new infections caused by a single infected individual in a fully susceptible population. Many models describing competition for hosts between non-interacting pathogen strains in an infinite population lead to the conclusion that selection favors invasion of new strains if and only if they have higher R0 values than the resident. Here we demonstrate that this picture fails in finite populations. Using a simple stochastic SIS model, we show that in general there is no analogous optimization principle. We find that successive invasions may in some cases lead to strains that infect a smaller fraction of the host population, and that mutually invasible pathogen strains exist. In the limit of weak selection we demonstrate that an optimization principle does exist, although it differs from R0 maximization. For strains with very large R0, we derive an expression for this local fitness function and use it to establish a lower bound for the error caused by neglecting stochastic effects. Furthermore, we apply this weak selection limit to investigate the selection dynamics in the presence of a trade-off between the virulence and the transmission rate of a pathogen."}],"publist_id":"5166","acknowledgement":"J.H. received support from the Zdenek Bakala Foundation and the Mobility Fund of Charles University in Prague.","volume":360,"status":"public","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","publication":"Journal of Theoretical Biology","_id":"1928","scopus_import":1,"author":[{"full_name":"Humplik, Jan","first_name":"Jan","last_name":"Humplik","id":"2E9627A8-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Hill, Alison","last_name":"Hill","first_name":"Alison"},{"full_name":"Nowak, Martin","last_name":"Nowak","first_name":"Martin"}],"oa_version":"None","publication_status":"published","date_created":"2018-12-11T11:54:46Z","department":[{"_id":"GaTk"}],"month":"11","title":"Evolutionary dynamics of infectious diseases in finite populations","intvolume":"       360","page":"149 - 162","language":[{"iso":"eng"}],"publisher":"Elsevier"}]